Quantum-Inspired Algorithm Design for Large-Scale Data Compression

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Traditional data compression algorithms, primarily based on classical statistical modeling, often struggle to achieve optimal compression ratios when dealing with large-scale, complex datasets. This paper proposes a novel approach to data compression—quantum-inspired algorithm design—that addresses this limitation. The core idea is to simulate the principles of quantum superposition, allowing the compression system to simultaneously evaluate and consider multiple compression strategies based on the probabilistic characteristics of the data. This approach aims to overcome the inherent limitations of relying on a single, static model, leading to more adaptive and potentially superior compression performance. We outline the design principles, key components, and theoretical underpinnings of this new methodology, emphasizing its potential impact on large-scale data compression challenges. The proposed framework offers a fundamentally different perspective, moving beyond deterministic models toward a probabilistic, superposition-based strategy.

DOI: 10.5281/zenodo.22241960

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